ClickHouse Analytics Database
Term 43 of 68 in the ERPStack technical glossary
What is ClickHouse Analytics Database?
ClickHouse is a fast, open-source columnar database management system (DBMS) designed for Online Analytical Processing (OLAP), allowing companies to query billions of rows in milliseconds.
ClickHouse Analytics Database at a glance
- Storage model
- Columnar — a query reading 3 of 200 columns touches only those 3 on disk
- Best fit
- Aggregations over 100 million or more rows where seconds matter more than single-row lookups
- Worst fit
- High-frequency single-row updates; it is not a replacement for a PostgreSQL 18 transactional store
- Typical pairing
- PostgreSQL 18 for writes, ClickHouse for reads, kept in sync by an event stream
- Built with
- ClickHouse fed from PostgreSQL 18 over Apache Kafka, provisioned with Terraform on AWS, dashboards rendered by Next.js 16 and React 19, pipeline lag alerted through Sentry
- Numbers that matter
- 3 of 200 columns read instead of whole rows; 100 million rows is where it pays; 0 row-level updates; 6 hours is how stale a neglected pipeline gets
- Compare with
- TimescaleDB, MongoDB Atlas, Redis aggregates, or 1 PostgreSQL 18 read replica before any of them
- Commonly paired with
- Apache Kafka pipelines, CQRS read models, Next.js 16 and React 19 dashboards, Redis caches and Terraform provisioning
- Where it earns a place
- retail and logistics reporting over hundreds of millions of rows, and energy or telecom telemetry where a 3-second dashboard is the product rather than a nicety.
How ClickHouse Analytics Database works in production
The ERPStack approach to ClickHouse Analytics Database
We integrate ClickHouse for processing high-volume analytics, such as global supply chain tracking dashboards or real-time cybersecurity dashboards.
Frequently asked questions about ClickHouse Analytics Database
Why is a columnar Analytics Database faster for reporting?
Because it reads less. A row store fetches whole rows even when a query needs 3 fields; an Analytics Database organised by column reads only the columns referenced, and compresses each one well because neighbouring values are similar. On an aggregate over hundreds of millions of rows that difference is not incremental — it changes a query from minutes to seconds.
Can ClickHouse replace your transactional database?
No, and trying is a common expensive mistake. An Analytics Database of this kind is optimised for large scans and bulk inserts, not for updating 1 row thousands of times a second with full transactional guarantees. The correct architecture keeps PostgreSQL 18 as the system of record and feeds a derived analytical copy, which is the read side of a CQRS split.
When is it too early to add ClickHouse?
While PostgreSQL still answers your reports acceptably. Adding an Analytics Database means a second store, a synchronisation pipeline and a new class of consistency bug where the analytics lag or diverge. That cost is worth paying when analytical scans are measurably harming transactional latency and indexing, partitioning and read replicas have already been tried. It is not worth paying for a dashboard nobody has complained about.
How does data get from Postgres into ClickHouse?
Through an explicit pipeline, usually event-based. Changes are published — often via Apache Kafka — and applied to the Analytics Database in batches, because columnar stores prefer bulk inserts to single-row writes. That pipeline is real infrastructure with its own monitoring and backfill story, and treating it as an afterthought is how teams end up with dashboards that are quietly 6 hours stale.